Poh, Karen Chin (2018-12). Determinants of Spatial and Temporal Variation of West Nile Virus Transmission in Texas. Doctoral Dissertation. Thesis uri icon

abstract

  • West Nile virus (WNV) is a zoonotic vector-borne virus that infects avian and mammal hosts. In Texas, WNV was first reported in 2002 in Harris County and has since been reported annually throughout the state. With variable funding available for mosquito surveillance in Texas, predictive modeling is an economical method for mosquito control, but has not been parameterized for major metropolitan areas of central and southeast Texas. Thus, this dissertation uses historical databases to create predictive models that are specifically tailored for major cities in Texas. To investigate the 2012 WNV epidemic in Dallas County, TX, logistic regression models identified an index of urbanization (composed of greater population density, lower normalized difference vegetation index, higher coverage of urban land types, and more impervious surfaces), lower elevation, and older populations as key factors in predicting the risk of WNV in Culex quinquefasciatus. Our model was then extrapolated as a risk map, which highlighted north and central Dallas County as areas of high risk for WNV-positive mosquitoes. A similar study for Harris County was conducted, where the best-fit model found that areas with higher elevation, more impervious surfaces, greater median income, and predominantly Hispanic populations will have higher vector indexes, which measure the average number of WNV-infected female Culex mosquitoes collected per trap night. The predictive map based on this model emphasized high-risk areas in central and north Harris County. Harris County's long-term database was also used to investigate temporal patterns between vector abundance, WNV infection in Cx. quinquefasciatus, and weather patterns. A time-series analysis revealed correlations between abundance and environmental variability measurements, following our hypothesis of Schmalhausen's law that states organisms are susceptible to mean (average) temperature and precipitation measurements as well as extreme or variability in weather. The infection rate model identified temperature with an 8-month lag as a significant covariate for WNV infection rates, highlighting the importance of overwintering temperatures preceding the WNV season. These models (landscape, demographic, and meteorological conditions) can be used by local mosquito control agencies to predict WNV infection in Cx. quinquefasciatus for proactive and effective control efforts.
  • West Nile virus (WNV) is a zoonotic vector-borne virus that infects avian and mammal
    hosts. In Texas, WNV was first reported in 2002 in Harris County and has since been reported
    annually throughout the state. With variable funding available for mosquito surveillance in
    Texas, predictive modeling is an economical method for mosquito control, but has not been
    parameterized for major metropolitan areas of central and southeast Texas. Thus, this dissertation
    uses historical databases to create predictive models that are specifically tailored for major cities
    in Texas.
    To investigate the 2012 WNV epidemic in Dallas County, TX, logistic regression models
    identified an index of urbanization (composed of greater population density, lower normalized
    difference vegetation index, higher coverage of urban land types, and more impervious surfaces),
    lower elevation, and older populations as key factors in predicting the risk of WNV in Culex
    quinquefasciatus. Our model was then extrapolated as a risk map, which highlighted north and
    central Dallas County as areas of high risk for WNV-positive mosquitoes.
    A similar study for Harris County was conducted, where the best-fit model found that
    areas with higher elevation, more impervious surfaces, greater median income, and
    predominantly Hispanic populations will have higher vector indexes, which measure the average
    number of WNV-infected female Culex mosquitoes collected per trap night. The predictive map
    based on this model emphasized high-risk areas in central and north Harris County.
    Harris County's long-term database was also used to investigate temporal patterns
    between vector abundance, WNV infection in Cx. quinquefasciatus, and weather patterns. A
    time-series analysis revealed correlations between abundance and environmental variability
    measurements, following our hypothesis of Schmalhausen's law that states organisms are
    susceptible to mean (average) temperature and precipitation measurements as well as extreme or
    variability in weather. The infection rate model identified temperature with an 8-month lag as a
    significant covariate for WNV infection rates, highlighting the importance of overwintering
    temperatures preceding the WNV season.
    These models (landscape, demographic, and meteorological conditions) can be used by
    local mosquito control agencies to predict WNV infection in Cx. quinquefasciatus for proactive
    and effective control efforts.

publication date

  • December 2018